Hinton [1]} proposed to consider a fine-tuning as the final step for training a DBN, aiming to adjust the matrices \(\textbf {W}^i\) , \(i=1,2,\ldots ,L\) . The procedure is performed using Backpropagation or the Gradient descent algorithm. The idea is to minimize some error measure considering the output of an additional layer placed at the top of the DBN after the training procedure. The aforementioned layer is often composed of logistic units, a softmax, or even some supervised technique.
